Robust and Accurate Capture of Human Joint Pose Using an Inertial Sensor

October 31, 2018

Pubudu N. PathiranaM. Sajeewani KarunarathneGareth L. WilliamsPhan T. NamHugh Durrant-Whyte

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.


Robust and Accurate Capture of Human Joint Pose Using an Inertial Sensor
Wearable Inertial Measurement Units (IMU) measuring acceleration, earth magnetic field and gyroscopic measurements can be considered for capturing human skeletal postures in real time. Number of movement disorders require accurate and robust estimation of the human joint pose. Though these movements are inherently slow, the accuracy of estimation is vital as many subtle moment patterns such as tremor are useful to capture under many assessments scenarios. Also, as the end user is a patient with movement disabilities, the practical wearability aspects impose stringent requirements such as the use of minimal number of sensors as well as positioning them in conformable areas of the human body; particularly for longer term monitoring. Estimating skeletal and limb orientations to describe human posture dynamically via model based approaches poses numerous challenges. In this paper we convey that the use of measurement conversion ideas -a representation signifying a linear characterisation of an inherently non linear estimation problem, pragmatically improves the overall estimation of the limb orientation. A quaternion, as opposed to the euler angle based approach is adopted to avoid Gimbal lock scenarios. We also lay a systematic basis for quaternion normalisation, typically performed in the pre-filtering stage, by introducing an optimisation based mathematical justification. A robust version of the extended Kalman filter is configured to amalgamate the underlying ideas in enhancing the overall system performance while providing a structured and a comprehensive approach to IMU based real time human pose estimation problem, particularly in a movement disability capture context.



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